Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Context-awareness to increase inclusion of people with DS in society

Assistive technologies have the potential to enhance the quality of life of citizens. Most especially of interest are those cases where a person is affected by some physical or cognitive impairment. Whilst most work in this area have been focused on assisting people indoors to support their independence, the POSEIDON project is focused on empowering citizens with Down’s Syndrome to support their independence outdoors. This paper explains the POSEIDON module which we are in the process of developing to make the system context-aware,reactive and adaptive

A knowledge-based approach towards human activity recognition in smart environments

For many years it is known that the population of older persons is on the rise. A recent report estimates that globally, the share of the population aged 65 years or over is expected to increase from 9.3 percent in 2020 to around 16.0 percent in 2050 [1]. This point has been one of the main sources of motivation for active research in the domain of human activity recognition in smart-homes. The ability to perform ADL without assistance from other people can be considered as a reference for the estimation of the independent living level of the older person. Conventionally, this has been assessed by health-care domain experts via a qualitative evaluation of the ADL. Since this evaluation is qualitative, it can vary based on the person being monitored and the caregiver\u2019s experience. A significant amount of research work is implicitly or explicitly aimed at augmenting the health-care domain expert\u2019s qualitative evaluation with quantitative data or knowledge obtained from HAR. From a medical perspective, there is a lack of evidence about the technology readiness level of smart home architectures supporting older persons by recognizing ADL [2]. We hypothesize that this may be due to a lack of effective collaboration between smart-home researchers/developers and health-care domain experts, especially when considering HAR. We foresee an increase in HAR systems being developed in close collaboration with caregivers and geriatricians to support their qualitative evaluation of ADL with explainable quantitative outcomes of the HAR systems. This has been a motivation for the work in this thesis. The recognition of human activities \u2013 in particular ADL \u2013 may not only be limited to support the health and well-being of older people. It can be relevant to home users in general. For instance, HAR could support digital assistants or companion robots to provide contextually relevant and proactive support to the home users, whether young adults or old. This has also been a motivation for the work in this thesis. Given our motivations, namely, (i) facilitation of iterative development and ease in collaboration between HAR system researchers/developers and health-care domain experts in ADL, and (ii) robust HAR that can support digital assistants or companion robots. There is a need for the development of a HAR framework that at its core is modular and flexible to facilitate an iterative development process [3], which is an integral part of collaborative work that involves develop-test-improve phases. At the same time, the framework should be intelligible for the sake of enriched collaboration with health-care domain experts. Furthermore, it should be scalable, online, and accurate for having robust HAR, which can enable many smart-home applications. The goal of this thesis is to design and evaluate such a framework. This thesis contributes to the domain of HAR in smart-homes. Particularly the contribution can be divided into three parts. The first contribution is Arianna+, a framework to develop networks of ontologies - for knowledge representation and reasoning - that enables smart homes to perform human activity recognition online. The second contribution is OWLOOP, an API that supports the development of HAR system architectures based on Arianna+. It enables the usage of Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). The third contribution is the evaluation and exploitation of Arianna+ using OWLOOP API. The exploitation of Arianna+ using OWLOOP API has resulted in four HAR system implementations. The evaluations and results of these HAR systems emphasize the novelty of Arianna+

Collective awareness platforms and digital social innovation mediating consensus seeking in problem situations

In this paper we show the results of our studies carried out in the framework of the European Project SciCafe2.0 in the area of Participatory Engagement models. We present a methodological approach built on participative engagements models and holistic framework for problem situation clarification and solution impacts assessment. Several online platforms for social engagement have been analysed to extract the main patterns of participative engagement. We present our own experiments through the SciCafe2.0 Platform and our insights from requirements elicitation

DogOnt - Ontology Modeling for Intelligent Domotic Environments

Abstract. Home automation has recently gained a new momentum thanks to the ever-increasing commercial availability of domotic components. In this context, researchers are working to provide interoperation mechanisms and to add intelligence on top of them. For supporting intelligent behaviors, house modeling is an essential requirement to understand current and future house states and to possibly drive more complex actions. In this paper we propose a new house modeling ontology designed to fit real world domotic system capabilities and to support interoperation between currently available and future solutions. Taking advantage of technologies developed in the context of the Semantic Web, the DogOnt ontology supports device/network independent description of houses, including both “controllable ” and architectural elements. States and functionalities are automatically associated to the modeled elements through proper inheritance mechanisms and by means of properly defined SWRL auto-completion rules which ease the modeling process, while automatic device recognition is achieved through classification reasoning.

Application of context knowledge in supporting conceptual design decision making

Conceptual design is the most important phase of the product life cycle as the decisions taken at conceptual design stage affect the downstream phases (manufacture, assembly, use, maintenance, and disposal) in terms of cost, quality and function performed by the product. This research takes a holistic view by incorporating the knowledge related to the whole context (from the viewpoint of product, user, product's life cycle and environment in which the product operates) of a design problem for the consideration of the designer to make an informed decision making at the conceptual design stage. The design context knowledge comprising knowledge from these different viewpoints is formalised and a new model and corresponding computational framework is proposed to support conceptual design decision making using this formalised context knowledge. Using a case study, this paper shows the proof of the concept by selecting one concept among different design alternatives using design context knowledge thereby proactively supporting conceptual design decision making for an informed and effective decision making

Is Context-aware Reasoning = Case-based Reasoning?

The purpose of this paper is to explore the similarities and differences and then argue for the potential synergies between two methodologies, namely Context-aware Reasoning and Case-based Reasoning, that are amongst the tools which can be used for intelligent environment (IE) system development. Through a case study supported by a review of the literature, we argue that context awareness and case based reasoning are not equal and are complementary methodologies to solve a domain specific problem, rather, the IE development paradigm must build a cooperation between these two approaches to overcome the individual drawbacks and to maximise the success of the IE systems